Digitalization and Artificial Intelligence in Migration and Mobility: Transnational Implications of the COVID-19 Pandemic
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Digitalization and artificial intelligence (AI) technologies in migration and mobility have incrementally expanded over recent years. Iterative approaches to AI deployment experienced a surge during 2020 and into 2021, largely due to COVID-19 forcing greater reliance on advanced digital technology to monitor, inform and respond to the pandemic. This paper critically examines the implications of intensifying digitalization and AI for migration and mobility systems for a post-COVID transnational context. First, it situates digitalization and AI in migration by analyzing its uptake throughout the Migration Cycle. Second, the article evaluates the current challenges and, opportunities to migrants and migration systems brought about by deepening digitalization due to COVID-19, finding that while these expanding technologies can bolster human rights and support international development, potential gains can and are being eroded because of design, development and implementation aspects. Through a critical review of available literature on the subject, this paper argues that recent changes brought about by COVID-19 highlight that computational advances need to incorporate human rights throughout design and development stages, extending well beyond technical feasibility. This also extends beyond tech company references to inclusivity and transparency and requires analysis of systemic risks to migration and mobility regimes arising from advances in AI and related technologies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it